Purpose: First, to investigate the added diagnostic value of chest computed tomography (CT) for evaluating COVID-19 in symptomatic children by comparing chest CT findings with chest radiographic findings, and second, to identify the imaging signs and patterns on CT associated with COVID-19 pneumonia in children.
Materials And Methods: From March 2020 to December 2020, 56 consecutive children (33 males and 23 girls; mean age ± SD, 14.8 ± 5.0 years; range, 9 months-18 years) with mild to moderate symptom and laboratory confirmed COVID-19 (based on Centers for Disease Control criteria) underwent both chest radiography and chest CT on the same day within the first 2 days of initial presentation to the hospital. Two experienced radiologists independently evaluated chest radiographs and chest CT studies for thoracic abnormalities. The findings from chest radiography and chest CT were compared to evaluate the added diagnostic value of chest CT for affecting patient management. Interobserver agreement was measured with Cohen's κ statistics.
Results: Eleven (19.6%) of 56 patients had abnormal chest radiographic findings, including ground-glass opacity (GGO) in 5/11 (45.4%) and combined GGO and consolidation in 6/11 (54.5%). On chest CT, 26 (46.4%) of 56 patients had abnormal CT findings, including combined GGO and consolidation in 19/26 (73.1%), GGO in 6/26 (23.1%), and consolidation in 1/26 (3.8%). Chest CT detected all thoracic abnormalities seen on chest radiography in 11/26 (42.3%) cases. In 15/26 (57.7%), chest CT detected lung abnormalities that were not observed on chest radiography, which included GGO and consolidation in 9/15 (60%), GGO in 5/15 (33.3%), and consolidation in 1/15 (6.6%) cases. These additional CT findings did not affect patient management. In addition, chest CT detected radiological signs and patterns, including the halo sign, reversed halo sign, crazy paving pattern, and tree-in-bud pattern. There was almost perfect interobserver agreement between the two reviewers for detecting findings on both chest radiographs (κ, 0.89, p = .001) and chest CT (κ, 0.96, p = .001) studies.
Conclusion: Chest CT detected lung abnormalities, including GGO and/or consolidation, that were not observed on chest radiography in more than half of symptomatic pediatric patients with COVID-19 pneumonia. However, these additional CT findings did not affect patient management. Therefore, CT is not clinically indicated for the initial evaluation of mild to moderately symptomatic pediatric patients with COVID-19 pneumonia.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8014659 | PMC |
http://dx.doi.org/10.1002/ppul.25313 | DOI Listing |
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFEur J Trauma Emerg Surg
January 2025
Emergency Department, Tel Aviv Sourasky Medical Center, 6 Weizman Street, 6423906, Tel Aviv, Israel.
Objective: To evaluate the NEXUS Chest CT ALL decision instrument (DI) in reducing unnecessary chest CT imaging in minor blunt trauma patients while preserving high sensitivity for detecting clinically meaningful injuries. Additionally, we examined the impact of delayed presentation, chronic disease, and anticoagulation/anti-aggregation medications on trauma outcomes.
Methods: This retrospective study included 853 adult minor blunt trauma patients who underwent chest CT in the emergency department (ED) of Tel-Aviv Sourasky Medical Center between 2018 and 2022.
J Intern Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).
Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.
J Cachexia Sarcopenia Muscle
February 2025
Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan.
Background: Chest computed tomography (CT) is a valuable tool for diagnosing and predicting the severity of coronavirus disease 2019 (COVID-19) and assessing extrapulmonary organs. Reduced muscle mass and visceral fat accumulation are important features of a body composition phenotype in which obesity and muscle loss coexist, but their relationship with COVID-19 outcomes remains unclear. In this study, we aimed to investigate the association between the erector spinae muscle (ESM) to epicardial adipose tissue (EAT) ratio (ESM/EAT) on chest CT and disease severity in patients with COVID-19.
View Article and Find Full Text PDFRadiol Case Rep
March 2025
Department of Radiology, Tenri Hospital, Nara, Japan.
We report the case of a 62-year-old male on long-term hemodialysis who was admitted to our hospital due to acute cerebral infarction associated with a cardiac calcified amorphous tumor (CAT). The patient presented with recurrent episodes of syncope and retrograde amnesia. Brain MRI identified multiple acute cerebral infarctions, while transthoracic echocardiography (TTE) revealed a 2.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!